RESEARCH & RESOURCES

Considerations for Cloud Data Quality Tool Solutions

TDWI Webinar Speaker: Aaron Fuller, Superior Data Strategies

Date: Thursday, October 19, 2017

Time: 9:00 a.m. PT, 12:00 p.m. ET

Webinar Abstract

Cloud software offerings have exploded in the data management and governance scene in a big way. Longstanding leaders in the data quality tool market are releasing cloud versions of their DQ platforms while upstart cloud-only competitors attempt to gain market share by selling more lightweight toolsets, often directly to business divisions rather than IT. Interesting hybrid architectures are also being tested, sometimes with multiple vendors and sometimes with multiple types of implementations of the same vendors’ tools.

The exponential growth in the need for data quality tools will continue because it is driven by the explosive growth in the size and diversity of enterprise data assets as well as constantly increasing requirements for accuracy in analytics and BI. There’s too much raw data and not enough controlled, cleansed, audited, and well-understood information in our organizations. Whatever our data quality tool approach may be, it must address that challenge.

As is always the case with data management choices, there isn’t one right answer about data quality tools for every organization. However, organizations that carefully consider the advantages, disadvantages, challenges, and opportunities that cloud data quality tools offer in the context of their particular business needs will likely benefit greatly from the cloud data quality tool trend.

In this webinar, we will talk about seven key considerations for organizations that are considering implementing data quality management tools in the cloud—including quick roll-out, necessary features, multivendor challenges, and security and privacy. Then we’ll hear from one leading vendor in the data quality platform market about how they are responding to the demand for cloud offerings, and how they think that the cloud impacts their customers’ decisions about how to architect successful data quality tool implementations.

You Will Learn:

The difference in roll-out time that is typically experienced in onsite versus cloud implementations

The factors that influence the cost of cloud versus onsite implementations and why cost isn’t usually the primary driver

Key features to look for in a data quality tool, particularly when evaluating newer, cloud-only vendor options